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1.  Data mining neocortical high-frequency oscillations in epilepsy and controls 
Brain  2011;134(10):2948-2959.
Transient high-frequency (100–500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100–250 Hz) and fast ripple (250–500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100–500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.
doi:10.1093/brain/awr212
PMCID: PMC3187540  PMID: 21903727
high-frequency oscillations; epilepsy; intracranial EEG
2.  Flexible, Foldable, Actively Multiplexed, High-Density Electrode Array for Mapping Brain Activity in vivo 
Nature Neuroscience  2011;14(12):1599-1605.
Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we have developed new devices integrating ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors connected using many fewer wires. We used this system to record novel spatial properties of brain activity in vivo, including sleep spindles, single-trial visual evoked responses, and electrographic seizures. Our electrode array allowed us to discover that seizures may manifest as recurrent spiral waves which propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface (BMI) devices.
doi:10.1038/nn.2973
PMCID: PMC3235709  PMID: 22081157
Multielectrode array; electrode array; flexible electronics; multiplexed electrode; cortical surface electrode; foldable electrode; ECoG; μECoG; brain machine interface; high temporal resolution; high spatial resolution; spindle; visual neuroscience; spiral wave; epilepsy; seizure; epileptiform spike; interhemispheric fissure; silicon nanoribbon
3.  Mining terabytes of submillimeter-resolution ECoG datasets for neurophysiologic biomarkers 
Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. We present innovations in both hardware and software that allow sampling and interpretation of data from brain networks from hundreds or thousands of sensors at submillimeter resolution. These innovations consist of novel flexible, active electrode arrays and unsupervised algorithms for detecting and classifying neurophysiologic biomarkers, specifically high frequency oscillations. We propose these innovations as the foundation for a new generation of closed loop diagnostic and therapeutic medical devices, and brain-machine interfaces.
doi:10.1109/IEMBS.2010.5627681
PMCID: PMC3132566  PMID: 21097061
4.  A Conformal, Bio-interfaced Class of Silicon Electronics for Mapping Cardiac Electrophysiology 
Science translational medicine  2010;2(24):24ra22.
The sophistication and resolution of current implantable medical devices are limited by the need connect each sensor separately to data acquisition systems. The ability of these devices to sample and modulate tissues is further limited by the rigid, planar nature of the electronics and the electrode-tissue interface. Here, we report the development of a class of mechanically flexible silicon electronics for measuring signals in an intimate, conformal integrated mode on the dynamic, three dimensional surfaces of soft tissues in the human body. We illustrate this technology in sensor systems composed of 2016 silicon nanomembrane transistors configured to record electrical activity directly from the curved, wet surface of a beating heart in vivo. The devices sample with simultaneous sub-millimeter and sub-millisecond resolution through 288 amplified and multiplexed channels. We use these systems to map the spread of spontaneous and paced ventricular depolarization in real time, at high resolution, on the epicardial surface in a porcine animal model. This clinical-scale demonstration represents one example of many possible uses of this technology in minimally invasive medical devices.
[Conformal electronics and sensors intimately integrated with living tissues enable a new generation of implantable devices capable of addressing important problems in human health.]
doi:10.1126/scitranslmed.3000738
PMCID: PMC3039774  PMID: 20375008
5.  Dissolvable Films of Silk Fibroin for Ultrathin, Conformal Bio-Integrated Electronics 
Nature materials  2010;9(6):511-517.
Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain-machine interfaces. This paper describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable or surgical devices.
doi:10.1038/nmat2745
PMCID: PMC3034223  PMID: 20400953
6.  Human Substantia Nigra Neurons Encode Unexpected Financial Rewards 
Science (New York, N.Y.)  2009;323(5920):1496-1499.
The brain's sensitivity to unexpected outcomes plays a fundamental role in an organism's ability to adapt and learn new behaviors. Emerging research suggests that midbrain dopaminergic neurons encode these unexpected outcomes. We used microelectrode recordings during deep brain stimulation surgery to study neuronal activity in the human substantia nigra (SN) while patients with Parkinson's disease engaged in a probabilistic learning task motivated by virtual financial rewards. Based on a model of the participants' expected reward, we divided trial outcomes into expected and unexpected gains and losses. SN neurons exhibited significantly higher firing rates after unexpected gains than unexpected losses. No such differences were observed after expected gains and losses. This result provides critical support for the hypothesized role of the SN in human reinforcement learning.
doi:10.1126/science.1167342
PMCID: PMC2839450  PMID: 19286561

Results 1-6 (6)